Given the increasingly severe fi scal situation in Japan, it is essential to enhance public understanding and interest in the policy of elimination of utility poles and to develop it in a planned and prioritized manner. This study aims to examine the prioritization of maintenance by quantifying the absolute number and density of roadside utility poles by road and region, with the feasibility of the policy of elimination of utility poles in mind. This study uses actual pole position information, combining location data for roads and buildings to visualize distance and density, and conducts spatial analysis of the priority of elimination of utility poles based on space. Finally, we suggest that the classifi cation of emergency transportation roads and traffi c volume can be used as clues in prioritizing future maintenance.

This paper proposes an efficient method for calculating place-based space-time accessibility measures based on the preprocessing of transport networks and illustrates a case study of using the proposed method. First, we argue from a literature review that the calculation of place-based space-time accessibility measures can be computationally intensive and therefore efficient computational methods for the calculation should be developed. Second, we develop a method that calculates time distances between pairs of nodes in a transport network and thereby reduces the computational time of calculating space-time accessibility measures. Third, we implement the proposed method using the opensource programming language Python and demonstrates a case study that calculates different types of space-time accessibility measures. Last, we argue from the case study that the method can be implemented without great difficulty, runs very fast and works as expected.

R language is becoming a robust geocomputation tool as it provides reproducibility. A new R package geojp has been developed to use various geographical data of Japan, including National Land Number Information data provided by Ministry of Land, Infrastructure and Transport. This paper introduces the new package which allows to download the data and read as an sf object. The paper also demonstrates spatial join features, which are incorporated in R/sf but have not been well explained. This paper introduces how to use Japanese geographical data with the new R package, and demonstrates spatial join with a case study of locational analysis of facilities, followed by statistical analyses.